Ornithopter Project

Flapping flight provides the high maneuverability necessary for operation in a partially structured indoor environment. To achieve robust intelligence for tasks such as search and indoor navigation, the maneuverability of the ornithopter will be combined with a learning approach which makes minimal assumptions about the nature of disturbances and obstacles. This approach will develop optimal control policies for single or multiple vehicles. Based
on globally optimal distributed reinforcement learning, we propose to develop algorithms for a set of ornithopters to cooperate in sensing and navigation among unmodelled obstacles such as doors and walls. Our research will be verified with full three dimensional dynamic simulation, a multi-tethered laboratory test-bed, as well as with actual indoor flying ornithopters.

Please see http://robotics.eecs.berkeley.edu/~ronf/Ornithopter
for current information.

Figure 1: Commercially available VAMP ornithopter with custom low mass electronics. Total mass is approximately 13 grams, including Bluetooth and cell phone camera.